Characterization of ecosystem responses to climatic controls using artificial neural networks

Global Change Biology

Volumn 16, Issue 10, 2010, Pages 2737-2749

  Moffat, Antje M ^a   Beckstein, Clemens ^b   Churkina, Galina ^c   Mund, Martina ^a   Heimann, Martin ^a  

^a MAX PLANCK INSTITUTE FOR BIOGEOCHEMISTRY   (Germany)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^c LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

Author keywords

Artificial neural networks (ANNs); Climatic controls; Ecological data mining; Ecosystem physiology; Eddy covariance carbon flux; FLUXNET; Hainich forest; Inductive modeling

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BROAD-LEAVED FOREST; CARBON FLUX; CLIMATE CHANGE; CLIMATE MODELING; DECIDUOUS FOREST; ECOSYSTEM RESPONSE; EDDY COVARIANCE; GLOBAL CLIMATE; PARAMETERIZATION;

GERMANY; HAINICH NATIONAL PARK; THURINGIA;

EID: 77956236814     PISSN: 13541013     EISSN: 13652486     Source Type: Journal    
DOI: 10.1111/j.1365-2486.2010.02171.x     Document Type: Article

References (28)

1. Abramowitz G. Towards a benchmark for land surface models. Geophysical Research Letters 2005, 32:L22702.
2. Baldocchi DD. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany 2008, 56:1-26.
3. Bishop CM. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford, UK
4. Burgermeister J. Missing carbon mystery: case solved? Nature Reports Climate Change 2007, 0708:36-37.
5. Cybenko GV. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 1989, 2:303-314.
6. Goeckede M, Rebmann C, Foken T. A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterisation of complex sites. Agricultural and Forest Meteorology 2004, 127:175-188.
7. Gove JH, Hollinger DY. Application of a dual unscented Kalman filter for simultaneous state and parameter estimation in problems of surface-atmosphere exchange. Journal of Geophysical Research-Atmospheres 2006, 111. doi, 10.1029/2005JD006021
8. Gu LH, Baldocchi D, Verma SB, Black TA, Vesala T, Falge EM, Dowty PR. Advantages of diffuse radiation for terrestrial ecosystem productivity. Journal of Geophysical Research-Atmospheres 2002, 107. D6, 4050, doi, 10.1029/2001JD001242
9. Heimann M, Reichstein M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 2008, 451:289-292.
10. Hempel CG, Oppenheim P. Studies in the logic of explanation. Philosophy of Science 1948, 15:135-175.
11. Observed effects of climate changes (Chapter 1.2). Climate Change 2007: Synthesis Report, Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007a, 31-33. IPCC, IPCC, Geneva, Switzerland
12. The Contemporary Carbon Budget (Chapter 7.3.2). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007b, 519-521. IPCC, Cambridge University Press, Cambridge, UK
13. Knohl A, Baldocchi DD. Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem. Journal of Geophysical Research-Biogeosciences 2008, 113. G02023, doi, 10.1029/2007JG000663
14. Knohl A, Schulze E-D, Kolle O, Buchmann N. Large carbon uptake by an unmanaged 250-year-old deciduous forest in Central Germany. Agricultural and Forest Meteorology 2003, 118:151-167.
15. Kutsch WL, Kolle O, Rebmann C, Knohl A, Ziegler W, Schulze E-D. Advection and resulting CO2 exchange uncertainty in a tall forest in central Germany. Ecological Applications 2008, 18:1391-1405.
16. Larcher W. Physiological Plant Ecology 2003, Springer-Verlag, Berlin, Heidelberg
17. Moffat AM. 2010, A new methodology to interpret high resolution measurements of net carbon fluxes between the terrestrial ecosystems and the atmosphere. Doctoral thesis, Friedrich Schiller University, Jena
18. Moffat AM, Papale D, Reichstein M. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology 2007, 147:209-232.
19. Papale D, Reichstein M, Aubinet M. Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 2006, 3:571-583.
20. Papale D, Valentini A. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology 2003, 9:525-535.
21. Richardson AD, Hollinger DY, Burba GG. A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agricultural and Forest Meteorology 2006, 136:1-18.
22. Richardson AD, Mahecha M, Falge E. Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals. Agricultural and Forest Meteorology 2008, 148:38-50.
23. Roderick ML, Farquhar GD, Berry SL, Noble IR. On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation. Oecologia 2001, 129:21-30.
24. Rojas R. Neural Networks - A Systematic Introduction 1996, Springer, Berlin, Heidelberg
25. Schulze E-D. Der CO2-Gaswechsel der Buche (Fagus silvatica L.) in Abhängigkeit von den Klimafaktoren im Freiland. Flora 1970, 159:177-232.
26. Stauch VJ, Jarvis AJ. A semi-parametric gap-filling model for eddy covariance CO2 flux time series data. Global Change Biology 2006, 12:1707-1716.
27. van de Laar P, Heskes T, Gielen S. Partial retraining: a new approach to input relevance determination. International Journal of Neural Systems 1999, 9:75-85.
28. Young PC, Jarvis AJ. 2002, Data-based Mechanistic Modelling and State Dependent Parameter Models. In: CRES Report Number TR/177. Centre for Research on Environmental Systems and Statistics, Lancaster University.